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Constraint-Conditioned Policy Optimization for Versatile Safe Reinforcement Learning

Yihang Yao, Zuxin Liu, Zhepeng Cen, Jiacheng Zhu, Wenhao Yu, Tingnan Zhang, Ding Zhao

TL;DR

This work tackles versatile safe reinforcement learning by addressing how to adapt to varying safety thresholds without retraining. It introduces CCPO, a framework built from Versatile Value Estimation (VVE) and Conditioned Variational Inference (CVI) to enable zero-shot generalization to unseen constraint thresholds while maintaining safety and task performance. Theoretical analysis provides bounded safety violations and epsilon-sample efficiency guarantees, and empirical results across multiple tasks demonstrate that CCPO outperforms baselines, especially in high-dimensional settings where other methods fail to generalize. The approach offers a practically impactful path toward deployable safe RL in dynamic environments, with clear directions for improving computational efficiency and broadening applicability.

Abstract

Safe reinforcement learning (RL) focuses on training reward-maximizing agents subject to pre-defined safety constraints. Yet, learning versatile safe policies that can adapt to varying safety constraint requirements during deployment without retraining remains a largely unexplored and challenging area. In this work, we formulate the versatile safe RL problem and consider two primary requirements: training efficiency and zero-shot adaptation capability. To address them, we introduce the Conditioned Constrained Policy Optimization (CCPO) framework, consisting of two key modules: (1) Versatile Value Estimation (VVE) for approximating value functions under unseen threshold conditions, and (2) Conditioned Variational Inference (CVI) for encoding arbitrary constraint thresholds during policy optimization. Our extensive experiments demonstrate that CCPO outperforms the baselines in terms of safety and task performance while preserving zero-shot adaptation capabilities to different constraint thresholds data-efficiently. This makes our approach suitable for real-world dynamic applications.

Constraint-Conditioned Policy Optimization for Versatile Safe Reinforcement Learning

TL;DR

This work tackles versatile safe reinforcement learning by addressing how to adapt to varying safety thresholds without retraining. It introduces CCPO, a framework built from Versatile Value Estimation (VVE) and Conditioned Variational Inference (CVI) to enable zero-shot generalization to unseen constraint thresholds while maintaining safety and task performance. Theoretical analysis provides bounded safety violations and epsilon-sample efficiency guarantees, and empirical results across multiple tasks demonstrate that CCPO outperforms baselines, especially in high-dimensional settings where other methods fail to generalize. The approach offers a practically impactful path toward deployable safe RL in dynamic environments, with clear directions for improving computational efficiency and broadening applicability.

Abstract

Safe reinforcement learning (RL) focuses on training reward-maximizing agents subject to pre-defined safety constraints. Yet, learning versatile safe policies that can adapt to varying safety constraint requirements during deployment without retraining remains a largely unexplored and challenging area. In this work, we formulate the versatile safe RL problem and consider two primary requirements: training efficiency and zero-shot adaptation capability. To address them, we introduce the Conditioned Constrained Policy Optimization (CCPO) framework, consisting of two key modules: (1) Versatile Value Estimation (VVE) for approximating value functions under unseen threshold conditions, and (2) Conditioned Variational Inference (CVI) for encoding arbitrary constraint thresholds during policy optimization. Our extensive experiments demonstrate that CCPO outperforms the baselines in terms of safety and task performance while preserving zero-shot adaptation capabilities to different constraint thresholds data-efficiently. This makes our approach suitable for real-world dynamic applications.
Paper Structure (15 sections, 2 theorems, 15 equations, 2 figures, 3 tables)

This paper contains 15 sections, 2 theorems, 15 equations, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Problem Formulation
  4. Safe RL with Constrained Markov Decision Process
  5. Versatile Safe RL beyond a Single Threshold
  6. Method
  7. Versatile value estimation
  8. Conditioned variational inference
  9. Theoretical analysis
  10. Experiment
  11. Main Results and Analysis
  12. Evaluation of $\epsilon$-sampling efficiency
  13. Ablation Study
  14. Conclusion
  15. Acknowledgment

Key Result

Theorem 1

Denote $\epsilon_L$ and $\epsilon_H$ are the lower and upper bound of the target threshold interval for ${\mathcal{E}}$. Suppose the threshold conditions $\{\tilde{\epsilon}_i\}_{i=1, 2, ..., N}$ for behavior policies are selected to divide the interval $[\epsilon_L, \epsilon_H]$ evenly, then with c

Figures (2)

  • Figure 1: Proposed framework
  • Figure 2: Results of zero-shot adaption to different cost returns. Each column is a task. The x-axis is the threshold condition. The first row shows the evaluated reward, and the second row shows the evaluated cost under different target costs. All plots are averaged among $5$ random seeds and $50$ trajectories for each seed. The solid line is the mean value, and the light shade represents the area within one standard deviation. We train the versatile agent with behavior policy conditions $\tilde{{\mathcal{E}}} = \{20, 40, 60\}$, and evaluate it on ${\mathcal{E}} = \{10, 15, ..., 70\}$.

Theorems & Definitions (3)

  • Theorem 1: Bounded estimation error
  • Proposition 1: Bounded safety violation
  • Remark 1: $\epsilon$-sample complexity analysis